Developed predictive models:
- Identify and predict most important key measurements related to system velocity using railroad related key measurements data(HD data) using LASSO regression, H-clustering, univariate and multivariate XGB algorithms. Created a RShiny app(Python, R, RShiny on windows and linux).
- Identify most significant terminal activities within inbound and outbound of the terminal and predict the total late arrival time using SVM for classification and regression, created a RShiny app (R, RShiny on windows and Linux).
Composite score estimation with respect to the goal to represent the importance of each key measurement and to get an overall idea about what is happening in the network.
Developed a predictive model for assessing pancreatic cancer risk factors using Bayesian analysis through information theory learning algorithms(R, BayesiaLab).
Developed a model for logistic regression with known random covariates through a simulation study(MATLAB).
Analyzed data collected from Surveillance, Epidemiology, and End Results (SEER) registry, adopted conditional survival analysis methods and identified invariant characteristics of common gastrointestinal (GI) cancers using R and MATLAB software.
Developed a method to estimate cancer resistant rate by stratification of gender, and geographical area(MATLAB).
Exploratory analysis of cancer incidence by age, gender, ethnicity, and state in the US considering data extracted from SEER registry during 1973-2012 period(R).
Predicted and compared survival estimates of non-small cell lung cancer patients using data extracted from National Cancer Data Base (NCDB) based on parametric and nonparametric survival methods(SAS, R).
Evaluating Random Forest model for survival estimation of non-small cell lung cancer risk factors(R).
Longitudinal study to assess hearing loss among employees using data from four different power plants(R).
Creating word clouds, document writing using Rmarkdown, experience with Tableau, SQL, prepare DashBoards using RShiny
Held group discussion sessions and graded assignments in Biostatistics II and Correlated Data Analysis.
Actively engaged preparing numerous assignments, answer sheets, held group discussion sessions during statistical lab hours, graded students’ assignments, and exams in Biostatistics I.
Created pdf version booklets of using SPSS and statistical probability calculator web applets.
Facilitated group discussion sessions in Biostatistics for Evidence Based Medicine course for second year medical students.
Taught Intermediate Algebra and College Algebra courses for undergraduate students and supervised Math Lab discussion sessions.
Sherman, S., Rathnayake, N., Mdzinarishvili, T., Invariant Characteristics of Carcinogenesis, PLOS One, 10(10), e0140405, 2015.
Rathnayake, N., From, S., Swift, A. Zhong, H., Approximation of Expected Values of Nonlinear Functions of Random Variables, ProQuest LLC, 2013.
Rathnayake, N., Introduction to Data Science with Applications, Women in Technology of the Heartland (WITH), Aug 2018.
Rathnayake, N., Luo, J., Kan, G.L., Known Random Covariates Logistic Regression Model, American Statistical Association, KUMC, April 2018.
Rathnayake, N., Farazi, E., Bagenda, D., Cheng, Z. X., Model performance of dynamic predictive model to assess pancreatic cancer risk, Poster presentation at the College of Public Health (COPH) Student Research Conference, April, 2018.
Rathnayake, N., Big Data Analysis using Machine Learning Techniques, Biostatistics Journal Club, UNMC, January 2018.
Rathnayake, N., Farazi, E., Bagenda, D., Luo, J., Chai, W., Ly, Q., Cui, J., Development of a predictive model to assess pancreatic cancer risk in the general population, Poster presentation at the Specialized Program of Research Excellence (SPORE) conference, 2017.
Rathnayake, N., Determination of Carcinogenic Characteristics of GI Cancers, Biostatistics Journal Club, UNMC, January 2015.
Rathnayake, N., Certificate of Data Visualization: Storytelling, LinkedIn Learning, April 2018. Certificate.
Rathnayake, N., Certificate of Completion Business Analytics Foundations Predictive Prescriptive And Experimental Analytics, LinkedIn Learning, April 2018. Certificate.
Rathnayake, N., Certificate of Completion The Data Science of Marketing, LinkedIn Learning, April 2018. Certificate.
Rathnayake, N., Certificate of The Essential Elements of Predictive Analytics and Data Mining, LinkedIn Learning, April 2018. Certificate
Rathnayake, N., Certificate of Integrating Tableau and R for Data Science, LinkedIn Learning, March 2018. Certificate
Rathnayake, N., Certificate of Statistics for Big Data, University of Washington, Seattle, July 2017. SL, USL, Reprod.Research.
Rathnayake, N., hglmbc2 R Package, Dec 2020, article.
Rathnayake, N., Tobacco Prevention Modeling, 2020, article.
Rathnayake, N., COVID19 Dashboard, April 2020, article.
Rathnayake, N., Shiny App Comparing Cornhuskers with Top Ranked 25 Teams in NCAA football 2018, December 2018 article.
Rathnayake, N., Estimating Validation Set Error, April 2017, article.
Rathnayake, N., How to Analyze High-Dimensional Data, November 2017, article.